This package includes a set of functions to analyse raw paired-pulse TMS data and normalise them
You can install the development version of PairedPulseTMS from GitHub with:
You can also install the development version of PairedPulseTMS from GitHub with:
Basic examples which show you how to run the code and what output to expect:
library(PairedPulseTMS)
data_wide <- data.frame(id = 999, t(rnorm(10, mean = 1, sd = 0.1)))
NORMALISE_PP_to_SP(data_wide, format = "Wide", number_of_triggers = 10 , normalise = "C")
#> ID TS_alone1 CS_TS_1 TS_alone2 CS_TS_2 TS_alone3 CS_TS_3
#> combined[indices, ] 999 1 1 1.449482 1.449482 1.070172 1.070172
#> TS_alone4 CS_TS_4 TS_alone5 CS_TS_5
#> combined[indices, ] 1.371998 1.371998 1.04447 1.04447
data_long <- data.frame(value = c(999, rnorm(10, mean = 1, sd = 0.1)))
NORMALISE_PP_to_SP(data_long, format = "Long", number_of_triggers = 10 , normalise = "A")
#> ID TS_alone1 CS_TS_1 TS_alone2 CS_TS_2 TS_alone3 CS_TS_3 TS_alone4
#> value 999 1 1.192838 1 1.028868 1 1.337118 1
#> CS_TS_4 TS_alone5 CS_TS_5
#> value 1.036081 1 1.136345
ID <- LETTERS[1:5]
dataframe_wide <- data.frame(ID = ID, matrix(stats::rnorm(5 * 16, mean = 1, sd = 0.1), nrow = 5, ncol = 16) )
NORMALISE_PP_to_SP_forDataframe(data = dataframe_wide, format = "Wide", number_of_triggers = 16 , normalise = 3)
#> ID TS_alone1 CS_TS_1 TS_alone2 CS_TS_2 TS_alone3 CS_TS_3 TS_alone4
#> 1 A 1 1 0.8352156 0.8352156 0.6720380 0.6720380 0.6262157
#> 2 B 1 1 1.3195233 1.3195233 1.1918426 1.1918426 1.0216590
#> 3 C 1 1 1.1658587 1.1658587 0.9563315 0.9563315 1.0140510
#> 4 D 1 1 1.1894307 1.1894307 1.2637724 1.2637724 1.1639374
#> 5 E 1 1 0.8993478 0.8993478 0.8703721 0.8703721 0.8764973
#> CS_TS_4 TS_alone5 CS_TS_5 TS_alone6 CS_TS_6 TS_alone7 CS_TS_7
#> 1 0.6262157 0.8307642 0.8307642 0.7463783 0.7463783 0.6572433 0.6572433
#> 2 1.0216590 1.1381886 1.1381886 1.2569309 1.2569309 0.9633065 0.9633065
#> 3 1.0140510 1.0638478 1.0638478 0.9655759 0.9655759 1.0689028 1.0689028
#> 4 1.1639374 1.1443736 1.1443736 1.0172555 1.0172555 1.3891045 1.3891045
#> 5 0.8764973 0.9452128 0.9452128 0.8845529 0.8845529 1.0751223 1.0751223
#> TS_alone8 CS_TS_8
#> 1 0.7151704 0.7151704
#> 2 1.3516258 1.3516258
#> 3 0.8811992 0.8811992
#> 4 1.2408277 1.2408277
#> 5 0.8270450 0.8270450
dataframe_long <- as.data.frame(t(dataframe_wide))
NORMALISE_PP_to_SP_forDataframe(data = dataframe_long, format = "Long", number_of_triggers = 16 , normalise = 4)
#> ID TS_alone1 CS_TS_1 TS_alone2 CS_TS_2 TS_alone3 CS_TS_3
#> 1 A 0 0.29472331 0 0.11465799 0 -0.10271706
#> 2 B 0 -0.12797844 0 0.14929207 0 0.04752215
#> 3 C 0 -0.09060481 0 0.06285322 0 -0.13525546
#> 4 D 0 -0.14058720 0 0.03288772 0 0.09351402
#> 5 E 0 0.06836166 0 -0.03772369 0 -0.07047276
#> TS_alone4 CS_TS_4 TS_alone5 CS_TS_5 TS_alone6 CS_TS_6 TS_alone7
#> 1 0 -0.17333677 0 0.109314046 0 0.002200696 0
#> 2 0 -0.10655082 0 0.001459617 0 0.100694538 0
#> 3 0 -0.07665196 0 -0.028712465 0 -0.125635342 0
#> 4 0 0.01122140 0 -0.005729712 0 -0.123478796 0
#> 5 0 -0.06345958 0 0.012016604 0 -0.054311247 0
#> CS_TS_7 TS_alone8 CS_TS_8
#> 1 -0.12497775 0 -0.04051101
#> 2 -0.16536196 0 0.17332983
#> 3 -0.02397202 0 -0.21707637
#> 4 0.18807214 0 0.07519147
#> 5 0.14079606 0 -0.12153439
ID <- LETTERS[1:5]
dataframe_wide <- data.frame(ID = ID, matrix(stats::rnorm(5 * 16, mean = 1, sd = 0.5), nrow = 5, ncol = 16) )
data <- NORMALISE_PP_to_SP_forDataframe(data = dataframe_wide, format = "Wide", number_of_triggers = 16 , normalise = 5)
plots <- plot_data_for_Dataframes(data, format = "Wide", col_line = "grey", col_dots = "grey", error_measure = "se")
The plotting function can also take dataframes with only one ID
data_wide <- data[1, ]
plots <- plot_data_for_Dataframes(data_wide, format = "Wide", col_line = "grey", col_dots = "grey", error_measure = "se")
#> Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
#> Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced
#> Warning in qt(conf.interval/2 + 0.5, datac$N - 1): NaNs produced